Recent research has identified discriminatory behavior of automated prediction algorithms towards groups identified on specific protected attributes (e.g., gender, ethnicity, age group, etc.). When deployed in real-world scenarios, such techniques may demonstrate biased predictions resulting in unfair outcomes. Recent literature has witnessed algorithms for mitigating such biased behavior mostly by adding convex surrogates of fairness metrics such as demographic parity or equalized odds in the loss function, which are often not easy to estimate. This research proposes a novel in-processing based GroupMixNorm layer for mitigating bias from deep learning models. The GroupMixNorm layer probabilistically mixes group-level feature statistics of samples across different groups based on the protected attribute. The proposed method improves upon several fairness metrics with minimal impact on overall accuracy. Analysis on benchmark tabular and image datasets demonstrates the efficacy of the proposed method in achieving state-of-the-art performance. Further, the experimental analysis also suggests the robustness of the GroupMixNorm layer against new protected attributes during inference and its utility in eliminating bias from a pre-trained network.

翻译：近期研究揭示了自动预测算法对基于特定保护属性（如性别、种族、年龄组等）识别的群体存在歧视性行为。当这些技术部署于现实场景时，可能表现出预测偏差，导致不公平结果。已有文献主要通过向损失函数添加人口统计均等或均等化几率等公平性度量的凸替代项来缓解此类偏差行为，然而这些替代项往往难以估计。本研究提出一种新颖的基于处理过程的GroupMixNorm层，用于减轻深度学习模型中的偏差。该层基于保护属性，以概率方式混合不同群体样本的群体级特征统计量。所提方法在最小化整体精度影响的前提下，改善了多项公平性度量指标。在基准表格数据集和图像数据集上的分析表明，所提方法在实现最先进性能方面具有有效性。此外，实验分析还揭示了GroupMixNorm层在推理过程中对抗新保护属性的鲁棒性，以及其在消除预训练网络偏差中的实用性。